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How Big Data, AI, IoT and Deep Learning Are Powering Modern Healthcare

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Big data, artificial intelligence (AI), internet of things (IoT) and deep learning (DL) are revolutionizing modern healthcare post pandemic. After having made remarkable improvements in finance, retail and marketing, big data, artificial intelligence, internet of things (IoT) and deep learning are now transforming healthcare. The volume of data involved in healthcare studies and analysis makes it a perfect use-case for these ground breaking technologies. Healthcare industry handles an immense load of data that is piling up every day. Sooner or later, we will need big data tools to transform healthcare information into relevant insights that can help the development of health services.


Connectivity and AI are fast-moving trains which must be caught

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When I first started working with the Internet of Things (IoT) nearly 10 years ago I used to lead presentations with a "the world is changing, and it's changing fast" mantra. Now, with the rise of new advanced technologies driven by artificial intelligence (AI) I simply start with "nothing is going to be like yesterday!". In this increasingly connected world, it is only by looking back that you can comprehend how quickly things have changed. In 1984, when I left secondary school and the original Apple MacIntosh computer went on sale, there were only 3,000 devices connected to the internet. In 2008, the number of connected devices surpassed the number of people on the planet – at nearly seven billion.


Understanding Agent Environment in AI - KDnuggets

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Before starting the article, it is important to understand what an agent in AI is. The agent is basically an entity that helps the AI, machine learning, or deep reinforcement learning to make a decision or trigger the AI to make a decision. In terms of software, it is defined as the entity which can take decisions and can make different decisions on the basis of changes in the environment, or after getting input from the external environment. In simpler words, the quick agent perceives external change and acts against it the better the results obtained from the model. Hence the role of the agent is always very important in artificial intelligence, machine learning, and deep learning.


Day 29 : 60 days of Data Science and Machine Learning Series

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ML clustering Project 2 ( Part 2)... “Day 29 : 60 days of Data Science and Machine Learning Series” is published by Naina Chaturvedi in Coders Mojo.


Google's Text-to-Image AI Can Make Any Wacky Image You Can Imagine

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Google has a new text-to-image AI that the company says beats the competition. Called Imagen, the program takes in text -- for example, "a photo of a Persian cat wearing a cowboy hat and red shirt playing a guitar on a beach" -- and outputs a result. Imagen can produce images that are photorealistic or an artistic rendering. Google's website for Imagen let's people people select text to change the resulting image. Imagen follows other text-to-image generators such as DALL-E, VQ-GAN CLIP and Latent Diffusion Models.


The Download: Clearview AI's hefty fine, and countries' monkeypox preparation

MIT Technology Review

Controversial facial recognition company Clearview AI has been fined more than $10 million by the UK's data protection watchdog for collecting the faces of UK citizens from the web and social media. The firm was also ordered to delete all of the data it holds on UK citizens. The move by the UK's Information Commissioner's Office (ICO) is the latest in a string of high-profile fines against the company as data protection authorities around the world eye tougher restrictions on its practices. Clearview AI boasts one of the world's largest databases of 20 billion images of people's faces that it has scraped off the internet from publicly available sources, such as social media, without their consent. Clients such as police departments pay for access to the database to look for matches.


The Download: DeepMind's AI shortcomings, and China's social media translation problem

MIT Technology Review

Earlier this month, DeepMind presented a new "generalist" AI model called Gato. The model can play the video game Atari, caption images, chat, and stack blocks with a real robot arm, the Alphabet-owned AI lab announced. All in all, Gato can do hundreds of different tasks. But while Gato is undeniably fascinating, in the week since its release some researchers have got a bit carried away. One of DeepMind's top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn't contain his excitement.


Best Examples Of Python Programming Jobs

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A dedicated Python Developer will be expected to understand the language at a higher level and be capable of using Python to accomplish any number of tasks, including but not limited to data collection and analytics, database creation, web development, design scripting, and automation. A Python Developer frequently collaborates with data collection and analytics to provide valuable answers and insight. Python is used in web development, machine learning, artificial intelligence, scientific computing, and academic research. Its growing popularity can be attributed to the data science community's embrace of artificial intelligence and machine learning. Machine-learning applications are being used to innovate organizations in education, healthcare, and finance.


How can India make its technology policy powerful, innovative, and secure?

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Can we ever rein in the Big Tech firms to foster indigenous innovation, stimulate balanced growth, and protect national sovereignty? Can we have a balanced set of rules and a clear framework to safeguard larger public interest? Can we check the weaponisation of the internet with balanced cybersecurity and secure data governance framework to make Google (Alphabet); Apple; Facebook (Meta); Amazon; and Microsoft, besides others, more responsible and resilient? Look around, Big Tech run most of the digital services that are integral and ubiquitous to our life. Our minds, economy, national security, democracy, and progress are invisibly controlled by a few technology firms.


Global Big Data Conference

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We were days away from IPO. We had raised $100 million in funding and exploded from a team of 50 in a garage to 600 in 18 months. One million technologists joined our platform. We were the next big deal. It was the turn of a new millennium.